Recent advancements in neural machine translation (NMT) have significantly improved the accuracy of translation from one language to another. However, challenges such as adherence to translation memories, context-specific terminologies, and consistent formality register remain pervasive hurdles. This presentation explores the integration of Large Language Models (LLMs) into the MT pipeline to address these specific issues, demonstrating substantial improvements in translation quality and contextual appropriateness.